This research paper explores the potential of utilizing eye-tracking technology to advance conjoint modeling. It explores how eye-tracking data can enhance data quality, validate models, and improve UX in conjoint questionnaires. Key inquiries include participant attention to text and images, the emergence of simplification heuristics, attribute sequencing effects, and the impact of pre-exercise learning sections. The study offers valuable insights into the integration of eye tracking in conjoint research and its implications on model performance.
